Camouflaged Anomaly Detection: A Stonefish-DBSCAN Approach for Secure Underwater Sensor Networks

DOI

10.2991/978-94-6463-754-0_33How to use a DOI?

Keywords

Underwater Sensor Networks; Malicious Node Detection; Data Aggregation; DBSCAN; Stonefish-DBSCAN; Routing Protocols

Abstract

Underwater Sensor Networks (USNs) are increasingly vulnerable to malicious node attacks, compromising data integrity and network performance. Inspired by the remarkable camouflage capabilities of stonefish, we propose a novel malicious node detection and data aggregation scheme for USNs, leveraging the principles of “Density-Based Spatial Clustering of Applications with Noise” (DBSCAN). Our approach, dubbed “Stonefish-DBSCAN,” combines the unique characteristics of underwater environments with the clustering capabilities of DBSCAN to identify and isolate malicious nodes. We investigate the performance of four routing protocols - LEACH, FCMMFO, ACO-MCMC, and SFODBSCAN - in Underwater Wireless Sensor Networks (UWSNs), with a focus on malicious node detection and data aggregation. Our results reveal that SFO-DBSCAN outperforms the other protocols in all metrics, including average lifetime, delay, throughput, security, and energy consumption. Specifically, SFO-DBSCAN achieves the longest average lifetime of 70 hours, the lowest delay, and the highest throughput, while demonstrating robust security performance and receiving the most packets. Our study recommends SFO-DBSCAN for implementation in UWSNs, due to its ability to achieve a longer average lifetime, lower delay, higher throughput, robust security, and lower energy consumption. We also suggest further research to explore the application of DBSCAN in other areas of UWSNs, such as data aggregation and node localization. By combining the strengths of stonefish-inspired camouflage detection and DBSCAN clustering, we provide a robust and efficient solution for securing underwater sensor networks.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P. Anandavalli
AU  - R. J. Kavitha
AU  - Ilakkiaselvan Dhandapani
AU  - V. Balamurugan
AU  - E. Elakkiyachelvan
AU  - T. Thamizhmani
PY  - 2025
DA  - 2025/06/30
TI  - Camouflaged Anomaly Detection: A Stonefish-DBSCAN Approach for Secure Underwater Sensor Networks
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 379
EP  - 389
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_33
DO  - 10.2991/978-94-6463-754-0_33
ID  - Anandavalli2025
ER  -